PB1760: PREDICTION OF DRUG SENSITIVITY AND GENETIC VULNERABILITY FOR ACUTE LEUKEMIA SUBTYPES USING GRAPH OF SIGNALING PATHWAY CORRELATIONS.
Authors: Timofey Lebedev, Anton Buzdin, Vladimir Prassolov
Journal: HemaSphere (2023)

Topic: 3. Acute myeloid leukemia - Biology & Translational Research Background: Pediatric acute leukemias (AML and ALL) have unique expression and mutation patterns distinct from adult leukemias. Drug and genetic screens performed on cell lines are a valuable tool for drug repurposing, however leukemic cell lines do not represent mutational landscape observed in pediatric patients. To overcome this limitation we use signaling pathway activity to correspond pediatric leukemia subtypes with cell line data on genetic dependencies and drug sensitivity. Aims: Our goal is to predict non-mutant drug targets using transcriptome analysis, which can be selectively targeted by existing drugs in certain pediatric AML and ALL subtypes. Methods: We used Oncobox algorithm to calculate signaling pathways activity based on gene expression. We analyzed DepMap gene fitness data from CRISPR and RNAi screens to find genes which silencing selectively affected AML or ALL cells. ChEMBL and DSIGDB databases were used to find drugs targeting AML and ALL dependency genes. We used TARGET RNA-seq datasets and collected 59 pediatric AML and ALL samples to identify signaling pathways associated with leukemic subtypes and disease relapse. Transcriptomic data was collected for 8 cell lines and 16 drugs were tested to test the ability of our approach to predict drug mechanisms of action. Results: Overall we found 473 dependency genes for AML and 989 for ALL, of which 65 genes were common. Next, we developed an algorithm that uses CHEMBL and DSIgDB databases to find drugs, which can targeted any of these dependency genes/proteins, and found 406 inhibitors for 18 targetable genes/proteins. Since most drugs have several targets, we used drug sensitivity data from GDSC and CCLE projects to select drugs, sensitivity to which correlates with dependency on their targets. Interestingly correlations varied between AML and ALL cells. Thus sensitivity to FLT3 inhibitors had good correlation for both AML and ALL cells, but sensitivity to JAK2 inhibitor ruxolitinib significantly correlated with JAK2 dependency only for AML cells, and sensitivity to HDAC inhibitors had good correlation with HDAC7 dependency for ALL, but not AML cells. Using correlations of signaling pathways activity with genetic dependencies and drug sensitivity in leukemic cell lines and with leukemia subtypes and relapse in patients we constructed a graph from nearly 80 000 interactions which connected leukemia subtypes with cell line dependencies. This graph, for example, can link certain disease subtype to drug sensitivities through mutual correlations with pathways. We verified ability of our graph to identify mechanisms of drug action by testing 16 drugs on 8 leukemia cell lines. Summary/Conclusion: We propose that considering dependencies of leukemic cells might improve therapeutic approaches for pediatric leukemia and that our algorithm can be useful for selection of relevant drug targets and drug which show selectivity towards particular targets in specific tumor cells. Keywords: Pediatric, Drug sensitivity, Signaling, Acute leukemia